The amount of structured and unstructured data is exploding at a phenomenal speed. Python and R are NOT the best tools when it comes to analyzing big data.
As more and more companies move to build their data infrastructure in the cloud, new distributed computing frameworks such as Hadoop and Spark emerged as distributed platforms. Data Scientists who analyze big data not only need to adapt these new tools but also need to deeply understand the data infrastructures, database systems, as well as how to build data science pipelines in the Cloud platforms such as AWS and Azure.
So you have seen big data-related keywords mentioned countless times in data scientist job descriptions but don’t know how to get started? Have you learned big data theory from Udemy or Udacity but still don’t know how to apply the big data tools to complete a complex project from end to end?
Fill out the inquiry form to learn about the course curriculum or talk to our learning advisor.
This advanced-level big data course teaches you the practical big data skills that you won’t be able to learn anywhere else. It covers several important topics such as distributed computing, cloud, real-time data ingestion, machine learning at scale, as well as how to deploy and operationalize machine learning models in production.
Big Data for Data Scientists is an 8-week advanced-level project-based course that teaches data scientists the necessary tools to work on large-scale data science problems. The entire course is built around an end-to-end real-time machine learning problem. Students will learn the most cutting-edge big data frameworks and tools such as Apache Spark, Amazon SageMaker, Databricks, MLflow, Kafka, Elasticsearch, and Airflow. Students will also learn how to train machine learning models at scale and deploy models at scale in real-time.
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